A variety of physical impairments limit the effective transmission of data signals over wireline and wireless channels, such as the frequency selective nature of the channels, which causes different frequency components of the input signal to be attenuated and phase-shifted differently. This causes the impulse response to span several symbol intervals, resulting in time-smearing and interference between successive transmitted input symbols, commonly known as intersymbol interference (ISI). The ISI resulting from the channel distortion, if left uncompensated, causes high error rates. The solution to the ISI problem is to design a receiver that employs a means for compensating or reducing the ISI in the received signal. The compensator for the ISI is called an equalizer.
There are two general classes of equalization techniques to mitigate ISI:    (a) Maximum likelihood sequence estimation (MLSE), where a dynamic programming algorithm is used to determine the most likely transmitted sequence, given observations of the received noisy and ISI-corrupted sequence and knowledge of the channel impulse response coefficients; and    (b) Sub-optimal equalizer structures like a linear equalizer (LE), where one simple finite impulse response (FIR) filter is used to mitigate ISI, or a non-linear decision feedback equalizer (DFE) that in addition to the feed-forward FIR filter, employs a feedback filter (FBF) on the previously detected symbols.MLSE uses a sequence of received signal samples over successive symbol intervals to make decisions about the transmitted symbols, and is optimal from a bit error rate (BER) perspective. However, MLSE has a computation complexity that grow exponentially with the length of the channel time dispersion, and in most channels of practical interest, such a large computational complexity is prohibitively expensive to implement. In sub-optimal structures like LE and DFE, data detection is done on a symbol-by-symbol basis and hence is much simpler to implement than the optimal MLSE. Linear equalization uses a linear filter with adjustable coefficients. Decision feedback equalization exploits the use of previous detected symbols to suppress the ISI in the present symbol being detected.
In a typical baseband digital transmission over wireline, such as a DS3/E3/STS-1 line, the signal is distorted and attenuated due to the channel characteristics, cross talk, noise and timing jitter. Traditionally, an analog equalizer is used at the receiver to compensate for ISI due to the channel, and an analog timing recovery unit is used to acquire the optimal instant for sampling the received signal. Unfortunately, this technique has a slow convergence rate and will not yield as good a performance as that achieved by a straightforward linear symbol spaced equalizer (with ideal timing). Moreover, an analog equalizer circuit is usually power hungry when compared to a digital solution.
Analog equalization techniques also have a low jitter tolerance. Classic analog equalization techniques can barely meet the jitter tolerance of 0.3 UIpp required by the standards set out in ITU-T Recommendation G.823—“The Control of Jitter and Wander Within Digital Networks which are based on the 2048 kbit/s Hierarchy”, March 1993; and Telecordia (Bellcore) GR-499-CORE—“Transport Systems Generic Requirements (TSGR): Common Requirements”, December 1995.
Other typical techniques of mitigating ISI are digital and use a symbol or partial-spaced equalization. They may use a nonlinear “blind” timing algorithm that precedes the equalizer (non-decision-directed timing) or one that uses the decisions (decision-directed timing). Such techniques are described in Proakis, J. G., Digital Communications, 3rd Edition, McGraw-Hill, 1995, pp. 358–365. Methods that are based on the equalizer decision cannot handle input frequency offsets; as the equalizer tracks the frequency offset, the main tap slowly moves from one tap location to the next, eventually causing equalization failure. On the other hand, prior art techniques that use a blind, non-decision-based timing recovery are not very robust in the presence of high frequency input jitter.
What is needed is an equalizer with much improved robustness in the presence of input jitter. Ideally, the equalizer should achieve at least 0.5 UIpp jitter tolerance for frequency jitter in the range from 20 to 800 kHz.